Forward the Original Title: An Illustrated Guide to Rei Network: A Simple and Clear Understanding of the Seamless Integration of AI Agents and Blockchain
The creation of the Rei framework was designed to bridge the communication gap between AI and blockchain.
When creating AI agents, a core challenge is how to enable them to learn, iterate, and grow flexibly while ensuring the consistency of their outputs. Rei provides a framework for sharing structured data between AI and blockchain, allowing AI agents to learn, optimize, and maintain a set of experiences and knowledge.
The emergence of this framework makes it possible to develop AI systems with the following capabilities:
AI and blockchain have significant differences in their core attributes, creating numerous challenges for their compatibility:
These differences create the following compatibility challenges:
Original picture from francesco, compiled by DeepChao TechFlow
Image originally from francesco, compiled by Deep Tide TechFlow
Rei offers a new solution that combines the strengths of AI and blockchain.
Image originally from francesco, compiled by Deep Tide TechFlow
Rather than forcing the integration of AI and blockchain—two fundamentally different systems—Rei serves as a “universal translator,” allowing smooth communication and collaboration between the two through a translation layer.
Image originally from francesco, compiled by Deep Tide TechFlow
The core goals of Rei include:
Image originally from francesco, compiled by Deep Tide TechFlow
The first application of this framework is Unit00x0 (Rei_00 - $REI), which has been trained as a quantitative analyst.
The cognitive architecture of Rei consists of the following four layers:
The Rei framework is built upon the following three core pillars:
Image originally from francesco, compiled by Deep Tide TechFlow
Here are the specific manifestations of these interactions:
Image originally from francesco, compiled by Deep Tide TechFlow
With this architecture, Rei agents are now able to conduct in-depth analysis of tokens by combining on-chain data, price fluctuations, social sentiment, and other multidimensional information.
More importantly, Rei can not only analyze data but also develop deeper understanding based on it. This is thanks to the ability to directly store her experiences and insights on the blockchain, making this information a part of her knowledge system, available for retrieval and continuous optimization of decision-making and overall experience.
Rei’s data sources include the Plotly and Matplotlib libraries (for chart plotting), Coingecko, Defillama, on-chain data, and social sentiment data from Twitter. By leveraging these diverse data sources, Rei provides comprehensive on-chain analysis and market insights.
With the update to Quant V2, Rei now supports the following types of analysis:
Additionally, as of January 2025, Rei supports on-chain token buy and sell functionality. She is equipped with a smart contract wallet based on the ERC-4337 standard, making transactions more convenient and secure.
(Deep Tide TechFlow Note: ERC-4337 is an Ethereum Improvement Proposal supporting account abstraction, aimed at enhancing the user experience.)
Rei’s smart contract allows operations to be delegated to her through user signature authorization, enabling Rei to autonomously manage its portfolio.
Here are Rei’s wallet addresses:
Image originally from francesco, compiled by Deep Tide TechFlow
The Rei framework is not limited to the financial sector and can be applied to the following broad scenarios:
Welcome to join the Deep Tide TechFlow official community
Telegram subscription group: https://t.me/TechFlowDaily
Official Twitter account: https://x.com/TechFlowPost
Official English Twitter account: https://x.com/DeFlow_Intern
Forward the Original Title: An Illustrated Guide to Rei Network: A Simple and Clear Understanding of the Seamless Integration of AI Agents and Blockchain
The creation of the Rei framework was designed to bridge the communication gap between AI and blockchain.
When creating AI agents, a core challenge is how to enable them to learn, iterate, and grow flexibly while ensuring the consistency of their outputs. Rei provides a framework for sharing structured data between AI and blockchain, allowing AI agents to learn, optimize, and maintain a set of experiences and knowledge.
The emergence of this framework makes it possible to develop AI systems with the following capabilities:
AI and blockchain have significant differences in their core attributes, creating numerous challenges for their compatibility:
These differences create the following compatibility challenges:
Original picture from francesco, compiled by DeepChao TechFlow
Image originally from francesco, compiled by Deep Tide TechFlow
Rei offers a new solution that combines the strengths of AI and blockchain.
Image originally from francesco, compiled by Deep Tide TechFlow
Rather than forcing the integration of AI and blockchain—two fundamentally different systems—Rei serves as a “universal translator,” allowing smooth communication and collaboration between the two through a translation layer.
Image originally from francesco, compiled by Deep Tide TechFlow
The core goals of Rei include:
Image originally from francesco, compiled by Deep Tide TechFlow
The first application of this framework is Unit00x0 (Rei_00 - $REI), which has been trained as a quantitative analyst.
The cognitive architecture of Rei consists of the following four layers:
The Rei framework is built upon the following three core pillars:
Image originally from francesco, compiled by Deep Tide TechFlow
Here are the specific manifestations of these interactions:
Image originally from francesco, compiled by Deep Tide TechFlow
With this architecture, Rei agents are now able to conduct in-depth analysis of tokens by combining on-chain data, price fluctuations, social sentiment, and other multidimensional information.
More importantly, Rei can not only analyze data but also develop deeper understanding based on it. This is thanks to the ability to directly store her experiences and insights on the blockchain, making this information a part of her knowledge system, available for retrieval and continuous optimization of decision-making and overall experience.
Rei’s data sources include the Plotly and Matplotlib libraries (for chart plotting), Coingecko, Defillama, on-chain data, and social sentiment data from Twitter. By leveraging these diverse data sources, Rei provides comprehensive on-chain analysis and market insights.
With the update to Quant V2, Rei now supports the following types of analysis:
Additionally, as of January 2025, Rei supports on-chain token buy and sell functionality. She is equipped with a smart contract wallet based on the ERC-4337 standard, making transactions more convenient and secure.
(Deep Tide TechFlow Note: ERC-4337 is an Ethereum Improvement Proposal supporting account abstraction, aimed at enhancing the user experience.)
Rei’s smart contract allows operations to be delegated to her through user signature authorization, enabling Rei to autonomously manage its portfolio.
Here are Rei’s wallet addresses:
Image originally from francesco, compiled by Deep Tide TechFlow
The Rei framework is not limited to the financial sector and can be applied to the following broad scenarios:
Welcome to join the Deep Tide TechFlow official community
Telegram subscription group: https://t.me/TechFlowDaily
Official Twitter account: https://x.com/TechFlowPost
Official English Twitter account: https://x.com/DeFlow_Intern